import cartopy.crs as ccrs
import intake
import uxarray as ux
cat_url = "https://digital-earths-global-hackathon.github.io/catalog/catalog.yaml"
cat = intake.open_catalog(cat_url).NCAR
cat
NCAR:
args:
path: https://digital-earths-global-hackathon.github.io/catalog/NCAR/catalog.yaml
description: catalog as visible from NCAR
driver: intake.catalog.local.YAMLFileCatalog
metadata:
catalog_dir: https://digital-earths-global-hackathon.github.io/catalog
# cat.walk(depth=-1)
list(cat)
['CERES_EBAF',
'ERA5',
'IR_IMERG',
'JRA3Q',
'MERRA2',
'casesm2_10km_nocumulus',
'icon_d3hp003',
'icon_d3hp003aug',
'icon_d3hp003feb',
'icon_ngc4008',
'ifs_tco3999-ng5_deepoff',
'ifs_tco3999-ng5_rcbmf',
'ifs_tco3999-ng5_rcbmf_cf',
'mpas_dyamond1',
'mpas_dyamond2',
'mpas_dyamond3',
'nicam_gl11',
'scream-dkrz',
'scream2D_hrly',
'scream_lnd',
'scream_ne120',
'tracking-d3hp003',
'um_Africa_km4p4_RAL3P3_n1280_GAL9_nest',
'um_CTC_km4p4_RAL3P3_n1280_GAL9_nest',
'um_SAmer_km4p4_RAL3P3_n1280_GAL9_nest',
'um_SEA_km4p4_RAL3P3_n1280_GAL9_nest',
'um_glm_n1280_CoMA9_TBv1p2',
'um_glm_n1280_GAL9',
'um_glm_n2560_RAL3p3',
'wrf_conus',
'wrf_samerica']
ds_mpas2 = cat.mpas_dyamond2(zoom=3).to_dask()
ds_mpas2
/glade/u/apps/opt/conda/envs/2025-digital-earths-global-hackathon/lib/python3.12/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
'dims': dict(self._ds.dims),
<xarray.Dataset> Size: 357MB
Dimensions: (time: 329, cell: 768, nVertLevels: 55,
nSoilLevels: 4, nVertLevelsP1: 56)
Coordinates:
* cell (cell) float64 6kB nan 1.0 2.0 ... 765.0 766.0 767.0
* nSoilLevels (nSoilLevels) float64 32B nan 1.0 2.0 3.0
* nVertLevels (nVertLevels) float64 440B nan 1.0 2.0 ... 53.0 54.0
* nVertLevelsP1 (nVertLevelsP1) float64 448B nan 1.0 ... 54.0 55.0
* time (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Data variables: (12/19)
air_temperature (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
h_oml (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
hu_oml (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
hv_oml (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
pressure (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
relhum (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
... ...
tslb (time, cell, nSoilLevels) float32 4MB dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
uReconstructMeridional (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
uReconstructZonal (time, cell, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
vegfra (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
w (time, cell, nVertLevelsP1) float32 57MB dask.array<chunksize=(1, 768, 56), meta=np.ndarray>
xice (time, cell) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>xarray.Dataset
- time: 329
- cell: 768
- nVertLevels: 55
- nSoilLevels: 4
- nVertLevelsP1: 56
- cell(cell)float64nan 1.0 2.0 ... 765.0 766.0 767.0
- long_name :
- HEALPix pixel index
- units :
- 1
- nside :
- 8
array([ nan, 1., 2., ..., 765., 766., 767.], shape=(768,))
- nSoilLevels(nSoilLevels)float64nan 1.0 2.0 3.0
- long_name :
- nSoilLevels
array([nan, 1., 2., 3.])
- nVertLevels(nVertLevels)float64nan 1.0 2.0 3.0 ... 52.0 53.0 54.0
- long_name :
- nVertLevels
array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54.]) - nVertLevelsP1(nVertLevelsP1)float64nan 1.0 2.0 3.0 ... 53.0 54.0 55.0
- long_name :
- nVertLevelsP1
array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.]) - time(time)datetime64[ns]2020-01-20 ... 2020-03-01
- long_name :
- time
- axis :
- T
- reference_date :
- 2000-01-01 00:00:00
array(['2020-01-20T00:00:00.000000000', '2020-01-20T03:00:00.000000000', '2020-01-20T06:00:00.000000000', ..., '2020-02-29T18:00:00.000000000', '2020-02-29T21:00:00.000000000', '2020-03-01T00:00:00.000000000'], shape=(329,), dtype='datetime64[ns]')
- air_temperature(time, cell, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- K
- long_name :
- air_temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - h_oml(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- m
- long_name :
- ocean mixed layer depth
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - hu_oml(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- m^2 s^{-1}
- long_name :
- ocean mixed layer integrated u (zonal velocity)
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - hv_oml(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- m^2 s^{-1}
- long_name :
- ocean mixed layer integrated v (meridional velocity)
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - pressure(time, cell, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- Pa
- long_name :
- Pressure
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - relhum(time, cell, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- percent
- long_name :
- Relative humidity
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - sh2o(time, cell, nSoilLevels)float32dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
- units :
- m3 m^{-3}
- long_name :
- soil equivalent liquid water
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - skintemp(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- K
- long_name :
- ground or water surface temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - smois(time, cell, nSoilLevels)float32dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
- units :
- m3 m^{-3}
- long_name :
- soil moisture
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - snow(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- kg m^{-2}
- long_name :
- snow water equivalent
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - snowh(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- m
- long_name :
- physical snow depth
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - sst(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- K
- long_name :
- sea-surface temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - t_oml(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- K
- long_name :
- ocean mixed layer temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - tslb(time, cell, nSoilLevels)float32dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
- units :
- K
- long_name :
- soil layer temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - uReconstructMeridional(time, cell, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- m s^{-1}
- long_name :
- Meridional component of reconstructed horizontal velocity at cell centers
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - uReconstructZonal(time, cell, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- m s^{-1}
- long_name :
- Zonal component of reconstructed horizontal velocity at cell centers
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - vegfra(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- unitless
- long_name :
- vegetation fraction
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - w(time, cell, nVertLevelsP1)float32dask.array<chunksize=(1, 768, 56), meta=np.ndarray>
- units :
- m s^{-1}
- long_name :
- Vertical velocity at vertical cell faces
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.98 MiB 168.00 kiB Shape (329, 768, 56) (1, 768, 56) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - xice(time, cell)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- unitless
- long_name :
- fractional area coverage of sea-ice
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray
- cellPandasIndex
PandasIndex(Index([ nan, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, ... 758.0, 759.0, 760.0, 761.0, 762.0, 763.0, 764.0, 765.0, 766.0, 767.0], dtype='float64', name='cell', length=768)) - nSoilLevelsPandasIndex
PandasIndex(Index([nan, 1.0, 2.0, 3.0], dtype='float64', name='nSoilLevels'))
- nVertLevelsPandasIndex
PandasIndex(Index([ nan, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0], dtype='float64', name='nVertLevels')) - nVertLevelsP1PandasIndex
PandasIndex(Index([ nan, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0], dtype='float64', name='nVertLevelsP1')) - timePandasIndex
PandasIndex(DatetimeIndex(['2020-01-20 00:00:00', '2020-01-20 03:00:00', '2020-01-20 06:00:00', '2020-01-20 09:00:00', '2020-01-20 12:00:00', '2020-01-20 15:00:00', '2020-01-20 18:00:00', '2020-01-20 21:00:00', '2020-01-21 00:00:00', '2020-01-21 03:00:00', ... '2020-02-28 21:00:00', '2020-02-29 00:00:00', '2020-02-29 03:00:00', '2020-02-29 06:00:00', '2020-02-29 09:00:00', '2020-02-29 12:00:00', '2020-02-29 15:00:00', '2020-02-29 18:00:00', '2020-02-29 21:00:00', '2020-03-01 00:00:00'], dtype='datetime64[ns]', name='time', length=329, freq=None))
Let us plot snow
# Open a HEALPix UX array
uxds = ux.UxDataset.from_healpix(ds_mpas2)
uxds
<xarray.UxDataset> Size: 357MB
Dimensions: (time: 329, n_face: 768, nVertLevels: 55,
nSoilLevels: 4, nVertLevelsP1: 56)
Coordinates:
cell (n_face) float64 6kB nan 1.0 2.0 ... 766.0 767.0
* nSoilLevels (nSoilLevels) float64 32B nan 1.0 2.0 3.0
* nVertLevels (nVertLevels) float64 440B nan 1.0 2.0 ... 53.0 54.0
* nVertLevelsP1 (nVertLevelsP1) float64 448B nan 1.0 ... 54.0 55.0
* time (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Dimensions without coordinates: n_face
Data variables: (12/19)
air_temperature (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
h_oml (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
hu_oml (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
hv_oml (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
pressure (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
relhum (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
... ...
tslb (time, n_face, nSoilLevels) float32 4MB dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
uReconstructMeridional (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
uReconstructZonal (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
vegfra (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
w (time, n_face, nVertLevelsP1) float32 57MB dask.array<chunksize=(1, 768, 56), meta=np.ndarray>
xice (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray><xarray.UxDataset> Size: 357MB
Dimensions: (time: 329, n_face: 768, nVertLevels: 55,
nSoilLevels: 4, nVertLevelsP1: 56)
Coordinates:
cell (n_face) float64 6kB nan 1.0 2.0 ... 766.0 767.0
* nSoilLevels (nSoilLevels) float64 32B nan 1.0 2.0 3.0
* nVertLevels (nVertLevels) float64 440B nan 1.0 2.0 ... 53.0 54.0
* nVertLevelsP1 (nVertLevelsP1) float64 448B nan 1.0 ... 54.0 55.0
* time (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Dimensions without coordinates: n_face
Data variables: (12/19)
air_temperature (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
h_oml (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
hu_oml (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
hv_oml (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
pressure (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
relhum (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
... ...
tslb (time, n_face, nSoilLevels) float32 4MB dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
uReconstructMeridional (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
uReconstructZonal (time, n_face, nVertLevels) float32 56MB dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
vegfra (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>
w (time, n_face, nVertLevelsP1) float32 57MB dask.array<chunksize=(1, 768, 56), meta=np.ndarray>
xice (time, n_face) float32 1MB dask.array<chunksize=(1, 768), meta=np.ndarray>uxarray.UxDataset
- time: 329
- n_face: 768
- nVertLevels: 55
- nSoilLevels: 4
- nVertLevelsP1: 56
- cell(n_face)float64nan 1.0 2.0 ... 765.0 766.0 767.0
- long_name :
- HEALPix pixel index
- units :
- 1
- nside :
- 8
array([ nan, 1., 2., ..., 765., 766., 767.], shape=(768,))
- nSoilLevels(nSoilLevels)float64nan 1.0 2.0 3.0
- long_name :
- nSoilLevels
array([nan, 1., 2., 3.])
- nVertLevels(nVertLevels)float64nan 1.0 2.0 3.0 ... 52.0 53.0 54.0
- long_name :
- nVertLevels
array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54.]) - nVertLevelsP1(nVertLevelsP1)float64nan 1.0 2.0 3.0 ... 53.0 54.0 55.0
- long_name :
- nVertLevelsP1
array([nan, 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.]) - time(time)datetime64[ns]2020-01-20 ... 2020-03-01
- long_name :
- time
- axis :
- T
- reference_date :
- 2000-01-01 00:00:00
array(['2020-01-20T00:00:00.000000000', '2020-01-20T03:00:00.000000000', '2020-01-20T06:00:00.000000000', ..., '2020-02-29T18:00:00.000000000', '2020-02-29T21:00:00.000000000', '2020-03-01T00:00:00.000000000'], shape=(329,), dtype='datetime64[ns]')
- air_temperature(time, n_face, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- K
- long_name :
- air_temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - h_oml(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- m
- long_name :
- ocean mixed layer depth
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - hu_oml(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- m^2 s^{-1}
- long_name :
- ocean mixed layer integrated u (zonal velocity)
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - hv_oml(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- m^2 s^{-1}
- long_name :
- ocean mixed layer integrated v (meridional velocity)
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - pressure(time, n_face, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- Pa
- long_name :
- Pressure
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - relhum(time, n_face, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- percent
- long_name :
- Relative humidity
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - sh2o(time, n_face, nSoilLevels)float32dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
- units :
- m3 m^{-3}
- long_name :
- soil equivalent liquid water
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - skintemp(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- K
- long_name :
- ground or water surface temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - smois(time, n_face, nSoilLevels)float32dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
- units :
- m3 m^{-3}
- long_name :
- soil moisture
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - snow(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- kg m^{-2}
- long_name :
- snow water equivalent
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - snowh(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- m
- long_name :
- physical snow depth
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - sst(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- K
- long_name :
- sea-surface temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - t_oml(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- K
- long_name :
- ocean mixed layer temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - tslb(time, n_face, nSoilLevels)float32dask.array<chunksize=(1, 768, 4), meta=np.ndarray>
- units :
- K
- long_name :
- soil layer temperature
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 3.86 MiB 12.00 kiB Shape (329, 768, 4) (1, 768, 4) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - uReconstructMeridional(time, n_face, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- m s^{-1}
- long_name :
- Meridional component of reconstructed horizontal velocity at cell centers
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - uReconstructZonal(time, n_face, nVertLevels)float32dask.array<chunksize=(1, 768, 55), meta=np.ndarray>
- units :
- m s^{-1}
- long_name :
- Zonal component of reconstructed horizontal velocity at cell centers
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.01 MiB 165.00 kiB Shape (329, 768, 55) (1, 768, 55) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - vegfra(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- unitless
- long_name :
- vegetation fraction
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - w(time, n_face, nVertLevelsP1)float32dask.array<chunksize=(1, 768, 56), meta=np.ndarray>
- units :
- m s^{-1}
- long_name :
- Vertical velocity at vertical cell faces
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 53.98 MiB 168.00 kiB Shape (329, 768, 56) (1, 768, 56) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - xice(time, n_face)float32dask.array<chunksize=(1, 768), meta=np.ndarray>
- units :
- unitless
- long_name :
- fractional area coverage of sea-ice
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray
- nSoilLevelsPandasIndex
PandasIndex(Index([nan, 1.0, 2.0, 3.0], dtype='float64', name='nSoilLevels'))
- nVertLevelsPandasIndex
PandasIndex(Index([ nan, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0], dtype='float64', name='nVertLevels')) - nVertLevelsP1PandasIndex
PandasIndex(Index([ nan, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0], dtype='float64', name='nVertLevelsP1')) - timePandasIndex
PandasIndex(DatetimeIndex(['2020-01-20 00:00:00', '2020-01-20 03:00:00', '2020-01-20 06:00:00', '2020-01-20 09:00:00', '2020-01-20 12:00:00', '2020-01-20 15:00:00', '2020-01-20 18:00:00', '2020-01-20 21:00:00', '2020-01-21 00:00:00', '2020-01-21 03:00:00', ... '2020-02-28 21:00:00', '2020-02-29 00:00:00', '2020-02-29 03:00:00', '2020-02-29 06:00:00', '2020-02-29 09:00:00', '2020-02-29 12:00:00', '2020-02-29 15:00:00', '2020-02-29 18:00:00', '2020-02-29 21:00:00', '2020-03-01 00:00:00'], dtype='datetime64[ns]', name='time', length=329, freq=None))
Show Grid Information
<uxarray.Grid> Original Grid Type: HEALPix Grid Dimensions: * n_face: 768 Grid Coordinates (Spherical): * face_lon: (768,) * face_lat: (768,) Grid Coordinates (Cartesian): Grid Connectivity Variables: Grid Descriptor Variables:
uxarray.UxDataset.uxgrid
- n_face: 768
- face_lon(n_face)float6445.0 50.62 39.38 ... -50.62 -45.0
- standard_name :
- longitude
- long name :
- Longitude of the center of each face
- units :
- degrees_east
array([ 45. , 50.625 , 39.375 , 45. , 56.25 , 61.875 , 50.625 , 56.25 , 33.75 , 39.375 , 28.125 , 33.75 , 45. , 50.625 , 39.375 , 45. , 67.5 , 73.125 , 61.875 , 67.5 , 78.75 , 84.375 , 73.125 , 83.57142857, 56.25 , 61.875 , 50.625 , 57.85714286, 70.71428571, 82.5 , 67.5 , 81. , 22.5 , 28.125 , 16.875 , 22.5 , 33.75 , 39.375 , 28.125 , 32.14285714, 11.25 , 16.875 , 5.625 , 6.42857143, 19.28571429, 22.5 , 7.5 , 9. , 45. , 52.5 , 37.5 , 45. , 63. , 78.75 , 56.25 , 75. , 27. , 33.75 , 11.25 , 15. , 45. , 67.5 , 22.5 , 45. , 135. , 140.625 , 129.375 , 135. , 146.25 , 151.875 , 140.625 , 146.25 , 123.75 , 129.375 , 118.125 , 123.75 , 135. , 140.625 , 129.375 , 135. , ... -135. , -129.375 , -140.625 , -135. , -123.75 , -118.125 , -129.375 , -123.75 , -146.25 , -140.625 , -151.875 , -146.25 , -135. , -129.375 , -140.625 , -135. , -45. , -22.5 , -67.5 , -45. , -15. , -11.25 , -33.75 , -27. , -75. , -56.25 , -78.75 , -63. , -45. , -37.5 , -52.5 , -45. , -9. , -7.5 , -22.5 , -19.28571429, -6.42857143, -5.625 , -16.875 , -11.25 , -32.14285714, -28.125 , -39.375 , -33.75 , -22.5 , -16.875 , -28.125 , -22.5 , -81. , -67.5 , -82.5 , -70.71428571, -57.85714286, -50.625 , -61.875 , -56.25 , -83.57142857, -73.125 , -84.375 , -78.75 , -67.5 , -61.875 , -73.125 , -67.5 , -45. , -39.375 , -50.625 , -45. , -33.75 , -28.125 , -39.375 , -33.75 , -56.25 , -50.625 , -61.875 , -56.25 , -45. , -39.375 , -50.625 , -45. ]) - face_lat(n_face)float644.78 9.594 9.594 ... -9.594 -4.78
- standard_name :
- latitude
- long name :
- Latitude of the center of each face
- units :
- degrees_north
array([ 4.78019185, 9.59406823, 9.59406823, 14.47751219, 14.47751219, 19.47122063, 19.47122063, 24.62431835, 14.47751219, 19.47122063, 19.47122063, 24.62431835, 24.62431835, 30. , 30. , 35.68533471, 24.62431835, 30. , 30. , 35.68533471, 35.68533471, 41.8103149 , 41.8103149 , 48.14120779, 35.68533471, 41.8103149 , 41.8103149 , 48.14120779, 48.14120779, 54.3409123 , 54.3409123 , 60.43443884, 24.62431835, 30. , 30. , 35.68533471, 35.68533471, 41.8103149 , 41.8103149 , 48.14120779, 35.68533471, 41.8103149 , 41.8103149 , 48.14120779, 48.14120779, 54.3409123 , 54.3409123 , 60.43443884, 48.14120779, 54.3409123 , 54.3409123 , 60.43443884, 60.43443884, 66.44353569, 66.44353569, 72.38756093, 60.43443884, 66.44353569, 66.44353569, 72.38756093, 72.38756093, 78.28414761, 78.28414761, 84.14973294, 4.78019185, 9.59406823, 9.59406823, 14.47751219, 14.47751219, 19.47122063, 19.47122063, 24.62431835, 14.47751219, 19.47122063, 19.47122063, 24.62431835, 24.62431835, 30. , 30. , 35.68533471, ... -35.68533471, -30. , -30. , -24.62431835, -24.62431835, -19.47122063, -19.47122063, -14.47751219, -24.62431835, -19.47122063, -19.47122063, -14.47751219, -14.47751219, -9.59406823, -9.59406823, -4.78019185, -84.14973294, -78.28414761, -78.28414761, -72.38756093, -72.38756093, -66.44353569, -66.44353569, -60.43443884, -72.38756093, -66.44353569, -66.44353569, -60.43443884, -60.43443884, -54.3409123 , -54.3409123 , -48.14120779, -60.43443884, -54.3409123 , -54.3409123 , -48.14120779, -48.14120779, -41.8103149 , -41.8103149 , -35.68533471, -48.14120779, -41.8103149 , -41.8103149 , -35.68533471, -35.68533471, -30. , -30. , -24.62431835, -60.43443884, -54.3409123 , -54.3409123 , -48.14120779, -48.14120779, -41.8103149 , -41.8103149 , -35.68533471, -48.14120779, -41.8103149 , -41.8103149 , -35.68533471, -35.68533471, -30. , -30. , -24.62431835, -35.68533471, -30. , -30. , -24.62431835, -24.62431835, -19.47122063, -19.47122063, -14.47751219, -24.62431835, -19.47122063, -19.47122063, -14.47751219, -14.47751219, -9.59406823, -9.59406823, -4.78019185])
- zoom :
- 3
- n_side :
- 8
- n_pix :
- 768
- nest :
- True
- source_grid_spec :
- HEALPix
ux_snow = uxds['snow']
ux_snow
<xarray.UxDataArray 'snow' (time: 329, n_face: 768)> Size: 1MB
dask.array<open_dataset-snow, shape=(329, 768), dtype=float32, chunksize=(1, 768), chunktype=numpy.ndarray>
Coordinates:
cell (n_face) float64 6kB nan 1.0 2.0 3.0 ... 764.0 765.0 766.0 767.0
* time (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Dimensions without coordinates: n_face
Attributes:
units: kg m^{-2}
long_name: snow water equivalent
healpix_nside: 8
healpix_dim_name: cell
original_mpas_spatial_dim: nCells<xarray.UxDataArray 'snow' (time: 329, n_face: 768)> Size: 1MB
dask.array<open_dataset-snow, shape=(329, 768), dtype=float32, chunksize=(1, 768), chunktype=numpy.ndarray>
Coordinates:
cell (n_face) float64 6kB nan 1.0 2.0 3.0 ... 764.0 765.0 766.0 767.0
* time (time) datetime64[ns] 3kB 2020-01-20 ... 2020-03-01
Dimensions without coordinates: n_face
Attributes:
units: kg m^{-2}
long_name: snow water equivalent
healpix_nside: 8
healpix_dim_name: cell
original_mpas_spatial_dim: nCellsuxarray.UxDataArray
'snow'
- time: 329
- n_face: 768
- dask.array<chunksize=(1, 768), meta=np.ndarray>
Array Chunk Bytes 0.96 MiB 3.00 kiB Shape (329, 768) (1, 768) Dask graph 329 chunks in 2 graph layers Data type float32 numpy.ndarray - cell(n_face)float64nan 1.0 2.0 ... 765.0 766.0 767.0
- long_name :
- HEALPix pixel index
- units :
- 1
- nside :
- 8
array([ nan, 1., 2., ..., 765., 766., 767.], shape=(768,))
- time(time)datetime64[ns]2020-01-20 ... 2020-03-01
- long_name :
- time
- axis :
- T
- reference_date :
- 2000-01-01 00:00:00
array(['2020-01-20T00:00:00.000000000', '2020-01-20T03:00:00.000000000', '2020-01-20T06:00:00.000000000', ..., '2020-02-29T18:00:00.000000000', '2020-02-29T21:00:00.000000000', '2020-03-01T00:00:00.000000000'], shape=(329,), dtype='datetime64[ns]')
- timePandasIndex
PandasIndex(DatetimeIndex(['2020-01-20 00:00:00', '2020-01-20 03:00:00', '2020-01-20 06:00:00', '2020-01-20 09:00:00', '2020-01-20 12:00:00', '2020-01-20 15:00:00', '2020-01-20 18:00:00', '2020-01-20 21:00:00', '2020-01-21 00:00:00', '2020-01-21 03:00:00', ... '2020-02-28 21:00:00', '2020-02-29 00:00:00', '2020-02-29 03:00:00', '2020-02-29 06:00:00', '2020-02-29 09:00:00', '2020-02-29 12:00:00', '2020-02-29 15:00:00', '2020-02-29 18:00:00', '2020-02-29 21:00:00', '2020-03-01 00:00:00'], dtype='datetime64[ns]', name='time', length=329, freq=None))
- units :
- kg m^{-2}
- long_name :
- snow water equivalent
- healpix_nside :
- 8
- healpix_dim_name :
- cell
- original_mpas_spatial_dim :
- nCells
Show Grid Information
<uxarray.Grid> Original Grid Type: HEALPix Grid Dimensions: * n_face: 768 Grid Coordinates (Spherical): * face_lon: (768,) * face_lat: (768,) Grid Coordinates (Cartesian): Grid Connectivity Variables: Grid Descriptor Variables:
uxarray.UxDataArray.uxgrid
- n_face: 768
- face_lon(n_face)float6445.0 50.62 39.38 ... -50.62 -45.0
- standard_name :
- longitude
- long name :
- Longitude of the center of each face
- units :
- degrees_east
array([ 45. , 50.625 , 39.375 , 45. , 56.25 , 61.875 , 50.625 , 56.25 , 33.75 , 39.375 , 28.125 , 33.75 , 45. , 50.625 , 39.375 , 45. , 67.5 , 73.125 , 61.875 , 67.5 , 78.75 , 84.375 , 73.125 , 83.57142857, 56.25 , 61.875 , 50.625 , 57.85714286, 70.71428571, 82.5 , 67.5 , 81. , 22.5 , 28.125 , 16.875 , 22.5 , 33.75 , 39.375 , 28.125 , 32.14285714, 11.25 , 16.875 , 5.625 , 6.42857143, 19.28571429, 22.5 , 7.5 , 9. , 45. , 52.5 , 37.5 , 45. , 63. , 78.75 , 56.25 , 75. , 27. , 33.75 , 11.25 , 15. , 45. , 67.5 , 22.5 , 45. , 135. , 140.625 , 129.375 , 135. , 146.25 , 151.875 , 140.625 , 146.25 , 123.75 , 129.375 , 118.125 , 123.75 , 135. , 140.625 , 129.375 , 135. , ... -135. , -129.375 , -140.625 , -135. , -123.75 , -118.125 , -129.375 , -123.75 , -146.25 , -140.625 , -151.875 , -146.25 , -135. , -129.375 , -140.625 , -135. , -45. , -22.5 , -67.5 , -45. , -15. , -11.25 , -33.75 , -27. , -75. , -56.25 , -78.75 , -63. , -45. , -37.5 , -52.5 , -45. , -9. , -7.5 , -22.5 , -19.28571429, -6.42857143, -5.625 , -16.875 , -11.25 , -32.14285714, -28.125 , -39.375 , -33.75 , -22.5 , -16.875 , -28.125 , -22.5 , -81. , -67.5 , -82.5 , -70.71428571, -57.85714286, -50.625 , -61.875 , -56.25 , -83.57142857, -73.125 , -84.375 , -78.75 , -67.5 , -61.875 , -73.125 , -67.5 , -45. , -39.375 , -50.625 , -45. , -33.75 , -28.125 , -39.375 , -33.75 , -56.25 , -50.625 , -61.875 , -56.25 , -45. , -39.375 , -50.625 , -45. ]) - face_lat(n_face)float644.78 9.594 9.594 ... -9.594 -4.78
- standard_name :
- latitude
- long name :
- Latitude of the center of each face
- units :
- degrees_north
array([ 4.78019185, 9.59406823, 9.59406823, 14.47751219, 14.47751219, 19.47122063, 19.47122063, 24.62431835, 14.47751219, 19.47122063, 19.47122063, 24.62431835, 24.62431835, 30. , 30. , 35.68533471, 24.62431835, 30. , 30. , 35.68533471, 35.68533471, 41.8103149 , 41.8103149 , 48.14120779, 35.68533471, 41.8103149 , 41.8103149 , 48.14120779, 48.14120779, 54.3409123 , 54.3409123 , 60.43443884, 24.62431835, 30. , 30. , 35.68533471, 35.68533471, 41.8103149 , 41.8103149 , 48.14120779, 35.68533471, 41.8103149 , 41.8103149 , 48.14120779, 48.14120779, 54.3409123 , 54.3409123 , 60.43443884, 48.14120779, 54.3409123 , 54.3409123 , 60.43443884, 60.43443884, 66.44353569, 66.44353569, 72.38756093, 60.43443884, 66.44353569, 66.44353569, 72.38756093, 72.38756093, 78.28414761, 78.28414761, 84.14973294, 4.78019185, 9.59406823, 9.59406823, 14.47751219, 14.47751219, 19.47122063, 19.47122063, 24.62431835, 14.47751219, 19.47122063, 19.47122063, 24.62431835, 24.62431835, 30. , 30. , 35.68533471, ... -35.68533471, -30. , -30. , -24.62431835, -24.62431835, -19.47122063, -19.47122063, -14.47751219, -24.62431835, -19.47122063, -19.47122063, -14.47751219, -14.47751219, -9.59406823, -9.59406823, -4.78019185, -84.14973294, -78.28414761, -78.28414761, -72.38756093, -72.38756093, -66.44353569, -66.44353569, -60.43443884, -72.38756093, -66.44353569, -66.44353569, -60.43443884, -60.43443884, -54.3409123 , -54.3409123 , -48.14120779, -60.43443884, -54.3409123 , -54.3409123 , -48.14120779, -48.14120779, -41.8103149 , -41.8103149 , -35.68533471, -48.14120779, -41.8103149 , -41.8103149 , -35.68533471, -35.68533471, -30. , -30. , -24.62431835, -60.43443884, -54.3409123 , -54.3409123 , -48.14120779, -48.14120779, -41.8103149 , -41.8103149 , -35.68533471, -48.14120779, -41.8103149 , -41.8103149 , -35.68533471, -35.68533471, -30. , -30. , -24.62431835, -35.68533471, -30. , -30. , -24.62431835, -24.62431835, -19.47122063, -19.47122063, -14.47751219, -24.62431835, -19.47122063, -19.47122063, -14.47751219, -14.47751219, -9.59406823, -9.59406823, -4.78019185])
- zoom :
- 3
- n_side :
- 8
- n_pix :
- 768
- nest :
- True
- source_grid_spec :
- HEALPix
%%time
projection = ccrs.Robinson(central_longitude=-135.5808361)
ux_snow.mean('time').plot(
projection=projection,
cmap="Blues",
features=["borders", "coastline"],
title="Global snow: Jan-March mean",
width=700,
)
CPU times: user 4.9 s, sys: 184 ms, total: 5.09 s
Wall time: 1.03 s
WARNING:param.GeoOverlayPlot02427: Due to internal constraints, when aspect and width/height is set, the bokeh backend uses those values as frame_width/frame_height instead. This ensures the aspect is respected, but means that the plot might be slightly larger than anticipated. Set the frame_width/frame_height explicitly to suppress this warning.